Results 171 to 180 of about 48,244 (304)
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin +7 more
wiley +1 more source
Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs). [PDF]
Dimitriadis SI +3 more
europepmc +1 more source
Universality for bounded degree spanning trees in randomly perturbed graphs
Julia Böttcher
openalex +1 more source
Topological Properties of International Commodity Market: How Uncertainty Affects the Linkages?
ABSTRACT The study aims to explore the network topology of the international commodity market by examining the interconnections among 21 commodity futures across various categories, including energy, precious and industrial metals, and agriculture. We analyze the market structure of these commodity futures under both low and high uncertainty conditions
Ibrahim Yagli, Bayram Deviren
wiley +1 more source
ABSTRACT EU member states have exhibited varying rates of apple production growth. Technical efficiency (TE) estimation is suitable for identifying best‐practice farm performance. This study examined whether the development of the apple sector in Germany, Italy, and Poland was influenced by production efficiency, access to technology, as well as ...
Anika Muder, Jakub Staniszewski
wiley +1 more source
AI in chemical engineering: From promise to practice
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Expected Lengths of Minimum Spanning Trees for Non-identical Edge Distributions [PDF]
Wenbo Li, Xinyi Zhang
openalex +1 more source
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar +3 more
wiley +1 more source

